| Course Unit Code | 460-4127/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Choice-compulsory type B |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| OH140 | RNDr. Eliška Ochodková, Ph.D. |
| KUD007 | doc. Mgr. Miloš Kudělka, Ph.D. |
| Summary |
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| In the course, students will be acquainted with basic and advanced algorithms for network analysis and visualization. Lectures will be devoted to the theoretical description of individual algorithms for individual analytical tasks, so that students are able to decide for themselves when which method is suitable, what its assumptions, what is its principle and what outputs can be obtained with it. The exercises will then be used for practical experiments on suitable datasets, experimenting with tools for analyzing network data and for evaluating experimental results. |
| Learning Outcomes of the Course Unit |
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| Learning outcomes of the course unit The aim of the course is to acquire knowledge related to the methods of network data analysis, especially the approaches associated with the measurement of local, global and time-varying network properties, algorithms for network structural properties analysis, generative network models and network representation structures. Students will be able to understand the analyzed data, will be able to correctly interpret and evaluate the results and will be able to present and visualize the results by suitable methods. |
| Course Contents |
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Lectures:
1. Networks and their properties, types of networks and their representation.
2. Methods of measuring the importance of peaks in networks
3. Structure and global properties of large networks, basic network models
4. Basic data structures for network representation and network analysis algorithms
5. Clusters in networks, matrix algorithms. dividing graph.
6. Sampling
7. Models of networks with community structure
8. Networking models for evolving networks
9. Modularity and community structure, detection of networks in networks
10. Correlation in networks
11. Network resistance and propagation of phenomena
12. Temporal networks
13. Multilayer networks, properties and measures, random walks and projections.
14. Network visualization methods
Exercises at the computer lab are thematically related to lectures, practical demonstrations, discussions and experiments. |
| Recommended or Required Reading |
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| Required Reading: |
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1. Albert-László Barabási. Network science. Cambridge university press, 2016. ISBN 978-1107076266
2. Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650.
3. Mark E. Dickison, Matteo Magnani, and Luca Rossi. Multilayer social networks. Cambridge University Press, 2016. |
1. Albert-László Barabási. Network science. Cambridge university press, 2016. ISBN 978-1107076266
2. Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650.
3. Mark E. Dickison, Matteo Magnani, and Luca Rossi. Multilayer social networks. Cambridge University Press, 2016. |
| Recommended Reading: |
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1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.
2. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]
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1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.
2. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]
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| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |